Home

Row

Tweets Today

42

Tweeters Today

26

#rstats Likes

967760

#rstats Tweets

101786

Row

Tweet volume

Tweets by Hour of Day

Row

💗 Most Liked Tweet Today

✨ Most Retweeted Tweet Today

🎉 Most Recent

Rankings

Row

Top Tweeters

User Engagement/Tweet
@v_matzek 2453.0000
@kaymwilliamson 1864.0000
@TheToadLady 1602.5000
@kiramhoffman 1138.0000
@adastephenson 1086.0000
@drhammed 892.0000
@SebastienPolis 875.0000
@kimistry8 689.0000
@hadleywickham 672.7143
@azandisarietta 492.0000

Where Engagement is RT * 2 + Favourite

Network of top tweeters

Relationships in the graph describe replies and quote retweets from the top tweeters that also have the hashtag.

Row

Top Words

Top Locations

Row

Top Hashtags

Hashtag Count
#DataScience 42575
#Python 40589
#IoT 33201
#MachineLearning 32282
#AI 32195
#Analytics 31829
#BigData 31361
#IIoT 27623
#Serverless 26349
#Linux 26165

Excluding #rstats and similar variations

Common co-occuring hashtags

Hashtags that occur together, grouped by community detection

Data

Tweets in the current week

---
title: "#rstats Twitter Explorer"
output: 
  flexdashboard::flex_dashboard:
    orientation: rows
    vertical_layout: scroll
    source_code: embed
    theme:
      version: 4
      bootswatch: yeti
    css: styles/main.css
---

```{r load_proj, include=FALSE}
devtools::load_all()
```

```{r load_packages, include=FALSE, cache=TRUE}
library(flexdashboard)
library(rtweet)
library(dplyr)
library(stringr)
library(tidytext)
library(lubridate)
library(echarts4r)
library(DT)

rstats_tweets <- read_twitter_csv("data/rstats_tweets.csv.gz") %>%
  mutate(created_at = as_datetime(created_at))
```


```{r time_data, include=FALSE, cache=TRUE}
count_timeseries <- rstats_tweets %>%
  ts_data(by = "hours")

tweets_week <- rstats_tweets %>%
  filter(date(created_at) %within% interval(floor_date(today(), "week"), today()))

tweets_today <- rstats_tweets %>%
  filter(date(created_at) == today())
```


```{r numbers, include=FALSE, cache=TRUE}
number_of_unique_tweets <- get_unique_value(rstats_tweets, text)

number_of_unique_tweets_today <-
  get_unique_value(tweets_today, text)

number_of_tweeters_today <- get_unique_value(tweets_today, user_id)

number_of_likes <- rstats_tweets %>%
  pull(favorite_count) %>%
  sum()
```


```{r rankings_data, include=FALSE, cache=TRUE}
top_tweeters <- rstats_tweets %>%
  group_by(user_id, screen_name, profile_url, profile_image_url) %>%
  summarize(engagement = (sum(retweet_count) * 2 + sum(favorite_count)) / n()) %>%
  ungroup() %>%
  slice_max(engagement, n = 10, with_ties = FALSE)

top_tweeters_format <- top_tweeters %>% 
  mutate(
    profile_url = stringr::str_glue("https://twitter.com/{screen_name}"),
    screen_name = stringr::str_glue('@{screen_name}'),
    engagement = formattable::color_bar("#a3c1e0", formattable::proportion)(engagement)
  ) %>%
  select(screen_name, engagement)

top_hashtags <- rstats_tweets %>%
  tidyr::separate_rows(hashtags, sep = " ") %>%
  count(hashtags) %>%
  filter(!(hashtags %in% c("rstats", "RStats"))) %>%
  slice_max(n, n = 10, with_ties = FALSE) %>%
  mutate(
    number = formattable::color_bar("plum", formattable::proportion)(n),
    hashtag = stringr::str_glue(
      '#{hashtags}'
    ),
  ) %>%
  select(hashtag, number)

word_banlist <-  c("t.co", "https", "rstats")
top_words <- rstats_tweets %>%
  select(text) %>%
  unnest_tokens(word, text) %>%
  anti_join(stop_words) %>%
  filter(!(word %in% word_banlist)) %>%
  filter(nchar(word) >= 4) %>% 
  count(word, sort = TRUE) %>%
  slice_max(n, n = 10, with_ties = FALSE) %>%
  select(word, n)

top_co_hashtags <- rstats_tweets %>% 
  unnest_tokens(bigram, hashtags, token = "ngrams", n = 2) %>% 
  tidyr::separate(bigram, c("word1", "word2"), sep = " ") %>% 
  filter(!word1 %in% c(stop_words$word, word_banlist)) %>% 
  filter(!word2 %in% c(stop_words$word, word_banlist)) %>% 
  count(word1, word2, sort = TRUE) %>% 
  filter(!is.na(word1) & !is.na(word2)) %>% 
  slice_max(n, n = 100, with_ties = FALSE)

top_locations <- rstats_tweets %>%
  filter(!is.na(location) & location != "#rstats") %>%
  distinct(user_id, .keep_all = TRUE) %>%
  mutate(location = str_replace_all(location, "London$", "London, England")) %>% 
  count(location) %>%
  slice_max(n, n = 10, with_ties = FALSE)
```


Home {data-icon="ion-home"}
====

Row
-----------------------------------------------------------------------

### Tweets Today

```{r tweets_today}
valueBox(number_of_unique_tweets_today, icon = "fa-comment-alt", color = "plum")
```

### Tweeters Today

```{r tweeters_today}
valueBox(number_of_tweeters_today, icon = "fa-user", color = "peachpuff")
```

### #rstats Likes

```{r likes}
valueBox(number_of_likes, icon = "fa-heart", color = "palevioletred")
```

### #rstats Tweets

```{r unique_tweets}
valueBox(number_of_unique_tweets, icon = "fa-comments", color = "mediumorchid")
```

Row {.tabset .tabset-fade}
-----------------------------------------------------------------------

### Tweet volume

```{r tweet_volume}
plot_tweet_volume(count_timeseries)
```

### Tweets by Hour of Day

```{r tweets_by_hour}
plot_tweet_by_hour(rstats_tweets)
```

Row
-----------------------------------------------------------------------

### 💗 Most Liked Tweet Today {.tweet-box}

```{r most_liked}
most_liked_url <- tweets_today %>%
  slice_max(favorite_count, with_ties = FALSE)

get_tweet_embed(most_liked_url$screen_name, most_liked_url$status_id)
```

### ✨ Most Retweeted Tweet Today {.tweet-box}

```{r most_rt}
most_retweeted <- tweets_today %>%
  slice_max(retweet_count, with_ties = FALSE)

get_tweet_embed(most_retweeted$screen_name, most_retweeted$status_id)
```

### 🎉 Most Recent {.tweet-box}

```{r most_recent}
most_recent <- tweets_today %>%
  slice_max(created_at, with_ties=FALSE)

get_tweet_embed(most_recent$screen_name, most_recent$status_id)
```

Rankings {data-icon="ion-arrow-graph-up-right"}
=========

Row
-----------------------------------------------------------------------

### Top Tweeters

```{r top_tweeters}
top_tweeters_format %>%
  knitr::kable(
    format = "html",
    escape = FALSE,
    align = "cll",
    col.names = c("User", "Engagement/Tweet "),
    table.attr = 'class = "table"'
  )
```

Where Engagement is `RT * 2 + Favourite`

### Network of top tweeters

Relationships in the graph describe replies and quote retweets from the top tweeters
that also have the hashtag.

```{r top_tweeters_net}
edgelist <-
  network_data(rstats_tweets %>% unflatten(), "reply,quote")
nodelist <- attr(edgelist, "idsn") %>%
  bind_cols()

top_edges <- edgelist %>%
  filter((from %in% top_tweeters$user_id) |
           (to %in% top_tweeters$user_id))

top_nodes <- nodelist %>%
  filter((id %in% top_edges$from) | (id %in% top_edges$to)) %>%
  mutate(is_top = ifelse((id %in% top_tweeters$user_id), "yes", "no"),
         size = 10)

e_charts() %>%
  e_graph() %>%
  e_graph_nodes(top_nodes, id, sn, size, category = is_top, legend = FALSE) %>%
  e_graph_edges(top_edges, from, to) %>%
  e_tooltip()
```

Row
-----------------------------------------------------------------------

### Top Words

```{r top_words}
top_words %>%
  e_charts(word) %>%
  e_bar(n, legend = FALSE) %>% 
  e_x_axis(
    axisLabel = list(
      interval = 0L,
      rotate = 30
    )
  ) %>%
  e_toolbox_feature("saveAsImage") %>%
  e_axis_labels(y = "Number of occurrences")
```

### Top Locations

```{r top_locations}
top_locations %>% 
  mutate(location = str_wrap(location, 9)) %>% 
  e_charts(location) %>% 
  e_bar(n, legend = FALSE) %>% 
  e_x_axis(
    axisLabel = list(
      interval = 0L,
      rotate = 30
    )
  ) %>%
  e_toolbox_feature("saveAsImage") %>%
  e_axis_labels(y = "Number of users from location")
```


Row
-----------------------------------------------------------------------

### Top Hashtags

```{r top_hashtags}
top_hashtags %>%
  knitr::kable(
    format = "html",
    escape = FALSE,
    align = "cll",
    col.names = c("Hashtag", "Count"),
    table.attr = 'class = "table"'
  )
```

Excluding `#rstats` and similar variations

### Common co-occuring hashtags

Hashtags that occur together, grouped by community detection

```{r co_hashtags}
top_co_hash_nodes <- tibble(
  nodes = c(top_co_hashtags$word1, top_co_hashtags$word2)
) %>% 
  distinct()

e_chart() %>% 
  e_graph() %>% 
  e_graph_nodes(top_co_hash_nodes, nodes, nodes, nodes) %>% 
  e_graph_edges(top_co_hashtags, word1, word2) %>% 
  e_modularity()
```


Data {data-icon="ion-stats-bars"}
==============

### Tweets in the current week {.datatable-container}

```{r datatable}
tweets_week %>%
  select(
    status_url,
    created_at,
    screen_name,
    text,
    retweet_count,
    favorite_count,
    mentions_screen_name
  ) %>%
  mutate(
    status_url = stringr::str_glue("On Twitter")
  ) %>%
  datatable(
    .,
    extensions = "Buttons",
    rownames = FALSE,
    escape = FALSE,
    colnames = c("Timestamp", "User", "Tweet", "RT", "Fav", "Mentioned"),
    filter = 'top',
    options = list(
      columnDefs = list(list(
        targets = 0, searchable = FALSE
      )),
      lengthMenu = c(5, 10, 25, 50, 100),
      pageLength = 10,
      scrollY = 600,
      scroller = TRUE,
      dom = '<"d-flex justify-content-between"lBf>rtip',
      buttons = list('copy', list(
        extend = 'collection',
        buttons = c('csv', 'excel'),
        text = 'Download'
      ))
    )
  )
```